What do RMSE values mean?
Root mean squared error (RMSE) is the square root of the mean of the square of all of the error. RMSE is a good measure of accuracy, but only to compare prediction errors of different models or model configurations for a particular variable and not between variables, as it is scale-dependent.
What does a high RMSE mean?
If the RMSE for the test set is much higher than that of the training set, it is likely that you’ve badly over fit the data, i.e. you’ve created a model that tests well in sample, but has little predictive value when tested out of sample.
How are RMSE and Mad values used in PLS?
Researchers need to compare RMSE and MAD values for alternative model set-ups and select the model, which minimizes RMSE and MAD values in the latent variable scores. In addition, to assess the results of a specific PLS path model, its predictive performance can be compared against two naïve benchmarks:
What does the analysis of PLS predict do?
The analysis serves as a diagnostic for possible overfitting of the PLS path model to the training data.
How does the LM approach differ from the PLS approach?
Instead, the LM approach regresses all exogenous indicator variables on each endogenous indicator variable to generate predictions. Thereby, a comparison with the PLS-SEM results offers information whether using a theoretically established path model improves (or at least does not worsen) the predictive performance of the available indicator data.
Which is better PLS path or PLS SEM?
If the Q² value is positive, the prediction error of the PLS-SEM results is smaller than the prediction error of simply using the mean values. In that case, the PLS-SEM models offers better predictive performance. (2) The linear regression model (LM) offers prediction errors and summary statistics that ignore the specified PLS path model.